The present invention relates generally to manufacturing and, more particularly, to the use of a time weighted moving average filter.
There is a constant drive within the semiconductor industry to increase the quality, reliability and throughput of integrated circuit devices, e.g., microprocessors, memory devices, and the like. This drive is fueled by consumer demands for higher quality computers and electronic devices that operate more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing the defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
Generally, a set of processing steps is performed on a group of wafers, sometimes referred to as a “lot,” using a variety of processing tools, including photolithography steppers, etch tools, deposition tools, polishing tools, rapid thermal processing tools, implantation tools, etc. The technologies underlying semiconductor processing tools have attracted increased attention over the last several years, resulting in substantial refinements. However, despite the advances made in this area, many of the processing tools that are currently commercially available suffer certain deficiencies. In particular, such tools often lack advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format, as well as event logging, real-time graphical display of both current processing parameters and the processing parameters of the entire run, and remote, i.e., local site and worldwide, monitoring. These deficiencies can engender non-optimal control of critical processing parameters, such as throughput, accuracy, stability and repeatability, processing temperatures, mechanical tool parameters, and the like. This variability manifests itself as within-run disparities, run-to-run disparities and tool-to-tool disparities that can propagate into deviations in product quality and performance, whereas an ideal monitoring and diagnostics system for such tools would provide a means of monitoring this variability, as well as providing means for optimizing control of critical parameters.
One technique for improving the operation of a semiconductor processing line includes using a factory wide control system to automatically control the operation of the various processing tools. The manufacturing tools communicate with a manufacturing framework or a network of processing modules. Each manufacturing tool is generally connected to an equipment interface. The equipment interface is connected to a machine interface that facilitates communications between the manufacturing tool and the manufacturing framework. The machine interface can generally be part of an advanced process control (APC) system. The APC system initiates a control script based upon a manufacturing model, which can be a software program that automatically retrieves the data needed to execute a manufacturing process. Often, semiconductor devices are staged through multiple manufacturing tools for multiple processes, generating data relating to the quality of the processed semiconductor devices.
During the fabrication process, various events may take place that affect the performance of the devices being fabricated. That is, variations in the fabrication process steps result in device performance variations. Factors, such as feature critical dimensions, doping levels, contact resistance, particle contamination, etc., all may potentially affect the end performance of the device. Various tools in the processing line are controlled in accordance with performance models to reduce processing variation. Commonly controlled tools include photolithography steppers, polishing tools, etching tools, and deposition tools. Pre-processing and/or post-processing metrology data is supplied to process controllers for the tools. Operating recipe parameters, such as processing time, are calculated by the process controllers based on the performance model and the metrology information to attempt to achieve post-processing results as close to a target value as possible. Reducing variation in this manner leads to increased throughput, reduced cost, higher device performance, etc., all of which equate to increased profitability. Metrology data collected before, during (i.e., in-situ), or after the processing of a wafer or lot of wafers may be used to generate feedback and/or feedforward information for use in determining a control action for the previous process tool (i.e., feedback), the subsequent process tool (i.e., feedforward), or both.
Typically, a controller adjusts an operating recipe for a controlled tool using feedback or feedforward metrology information. Control actions are typically generated using a control model that tracks one or more process state variables associate with the fabrication. For example, a controller may adjust a photolithography recipe parameter to control a critical dimension (CD) of the manufactured devices. Likewise, a controller may control an etch tool to affect a trench depth or a spacer width characteristic.
To provide stability for the controller, control actions are typically not generated based on just the most recently observed process state variable. Hence, the previous measurements are typically passed through an exponentially weighted moving average (EWMA) filter that outputs an average value for the process state that factors in the current and previous values. The EWMA filter is a weighted average in that the average is more heavily affected by the more recent state values.
EWMA filters have been employed for estimating process states for many years in the semiconductor industry. The general equation for an EWMA filter is:
                                                        y              ^                                      k              +              1                                =                                                                      ω                  0                                ⁢                                  y                  k                                            +                                                ω                  1                                ⁢                                  y                                      k                    -                    1                                                              +                                                ω                  2                                ⁢                                  y                                      k                    -                    2                                                              +              ⋯              +                                                ω                  n                                ⁢                                  y                                      k                    -                    n                                                                                                      ω                0                            +                              ω                1                            +                              ω                2                            +              ⋯              +                              ω                n                                                    ,                            (        1        )            where the weighting factor, ω1=(1−λ)1, discounts older measurements, and λ IS a tuning parameter that affects the level of discounting (i.e., 0<λ<1).
In cases where the number of measurements is large, the oldest measurements have a negligible contribution to the filtered value, and a recursive EWMA filter may be used:ŷk+1=(1−λ)ŷk+λyk.   (2)The recursive EWMA filter tracks only a previous process state estimate and updates the estimate as new data is received based on a weighting factor, λ.
The EWMA filter has several limitations when employed to a semiconductor environment. In a semiconductor fabrication environment, discrete processes are performed on individual wafers or groups of wafers (i.e., lots). Metrology data used to determine the process state is collected in separate discrete events. The metrology resources are shared to collect data regarding wafers of different types and at different stages of completion. Hence, the metrology data collected associated with a given process state is not received by the controller at constant update intervals.
Moreover, due to the number of independent process tools and metrology tools, the metrology data does not necessarily arrive in sequential time order. In other words, lots are not always measured in the same order as they were processed. The recursive EWMA filter does not account for out-of-order processing, as it assumes that wafers are processed sequentially. Samples are applied to generate new process state estimates as they are received. Also, once a sample has been incorporated in to the recursive EWMA state estimate, it cannot be undone. Also, the EWMA filter is not able to account for time significant time gaps between sequential processes. The EWMA discounts data identically whether there is a large time gap between sequential processes or a relatively short gap. A tool may drift during a large time gap, as the EWMA filter does not receive process state updates. Finally, an EWMA filter does not provide a quality metric for the process state estimate. An EWMA state estimate with old and sparse data is treated identically to new and well characterized data.
This section of this document is intended to introduce various aspects of art that may be related to various aspects of the present invention described and/or claimed below. This section provides background information to facilitate a better understanding of the various aspects of the present invention. It should be understood that the statements in this section of this document are to be read in this light, and not as admissions of prior art. The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.